Open Collections

Deep learning with limited labeled image data for health informatics

University of British Columbia

Deep learning is a data-driven technique for developing intelligent systems using a large amount of training data. Amongst the deep learning applications, this thesis focuses on problems in health informatics. Compared to the general deep learning applications, health informatics problems are complex, unique and pose problem-specific challenges. Many of these problems however, face a common challenge: the lack of labeled data. In this thesis, we explore the following three ways to overcome three specific image based health informatics problems: 1) The use of image patches instead of whole images as the input for deep learning. To increase the data size, each image is partitioned into non-overlapping, mid-level patches: This approach is illustrated by addressing the food image recognition problem. Automatic food recognition could be used for nutrition analysis. We propose a novel deep framework for mid-level food image patches. Evaluations on 3 benchmark datasets demonstrate that the proposed approach achieves superior performance over baseline convolutional neural networks (CNN) methods. 2) The use of prior knowledge to reduce the high dimensionality and complexity of raw data: We illustrate this idea on magnetic resonance imaging (MRI) images, for diagnosing a common mental-health disorder, the Attention Deficit Hyperactivity Disorder (ADHD). MRI has been increasingly used in analyzing ADHD with machine learning algorithms. We propose a multi-channel 3D CNN based automatic ADHD diagnosis approach using MRI scans. Evaluations on ADHD-200 Competition dataset show that the proposed approach achieves state-of-the-art accuracy. 3) The use of synthetic data pre-training along with real data domain adaptation to increase the available labeled data during training: We illustrate this idea on 2-D/3-D image registration problems. We propose a fully automatic and real-time CNN-based 2-D/3-D image registration system. Evaluations on Transesophageal Echocardiography (TEE) X-ray images from clinical studies demonstrate that the proposed system outperforms existing methods in accuracy and speed. We further propose a pairwise domain adaptation module (PDA MODULE), designed to be flexible for different deep learning-based 2-D/3-D registration frameworks with improved performance. Evaluations on two clinical applications demonstrate the PDA modules advantages for 2-D/3-D medical image registration with limited data.

Text

2018-04-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65665

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/